# !pip install git+https://github.com/alberanid/rotopy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask

# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")

nltk.download('all')

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[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package porter_test is already up-to-date!
[nltk_data]    | Downloading package wmt15_eval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all

True

# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
 
df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type
0	1	Snowpiercer	2013	18+	6.9	94%	NaN	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	English	Set seven years after the world has become a f...	60.0	tv series	3.0	1	0	0	0	1
1	2	Philadelphia	1993	13+	8.8	80%	NaN	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	English	The gang, 5 raging alcoholic, narcissists run ...	22.0	tv series	18.0	1	0	0	0	1
2	3	Roma	2018	18+	8.7	93%	NaN	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	English	In this British historical drama, the turbulen...	52.0	tv series	2.0	1	0	0	0	1
3	4	Amy	2015	18+	7.0	87%	NaN	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	English	A family drama focused on three generations of...	60.0	tv series	6.0	1	0	1	1	1
4	5	The Young Offenders	2016	NaN	8.0	100%	NaN	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	English	NaN	30.0	tv series	3.0	1	0	0	0	1

# profile = ProfileReport(df_tvshows)
# profile

def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation : 

# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
 
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
 
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
 
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
 
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation : 

df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	...	Set seven years after the world has become a f...	60	tv series	3	1	0	0	0	1	Netflix
1	2	Philadelphia	1993	13	8.8	80	NA	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	...	The gang, 5 raging alcoholic, narcissists run ...	22	tv series	18	1	0	0	0	1	Netflix
2	3	Roma	2018	18	8.7	93	NA	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	...	In this British historical drama, the turbulen...	52	tv series	2	1	0	0	0	1	Netflix
3	4	Amy	2015	18	7	87	NA	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	...	A family drama focused on three generations of...	60	tv series	6	1	0	1	1	1	Netflix
4	5	The Young Offenders	2016	NR	8	100	NA	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	...	NA	30	tv series	3	1	0	0	0	1	Netflix

5 rows × 21 columns

df_tvshows.describe()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
count	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.0
mean	2716.500000	2010.668446	0.341311	0.293999	0.403351	0.033689	1.0
std	1568.227662	11.726176	0.474193	0.455633	0.490615	0.180445	0.0
min	1.000000	1901.000000	0.000000	0.000000	0.000000	0.000000	1.0
25%	1358.750000	2009.000000	0.000000	0.000000	0.000000	0.000000	1.0
50%	2716.500000	2014.000000	0.000000	0.000000	0.000000	0.000000	1.0
75%	4074.250000	2017.000000	1.000000	1.000000	1.000000	0.000000	1.0
max	5432.000000	2020.000000	1.000000	1.000000	1.000000	1.000000	1.0

df_tvshows.corr()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
ID	1.000000	-0.031346	-0.646330	0.034293	0.441264	0.195409	NaN
Year	-0.031346	1.000000	0.222316	-0.065807	-0.198675	-0.022741	NaN
Netflix	-0.646330	0.222316	1.000000	-0.366515	-0.515086	-0.119344	NaN
Hulu	0.034293	-0.065807	-0.366515	1.000000	-0.377374	-0.075701	NaN
Prime Video	0.441264	-0.198675	-0.515086	-0.377374	1.000000	-0.151442	NaN
Disney+	0.195409	-0.022741	-0.119344	-0.075701	-0.151442	1.000000	NaN
Type	NaN	NaN	NaN	NaN	NaN	NaN	NaN

# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('Rotten Tomatoes', ascending = False)

# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
 
# udf_tvshows

# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]

df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]

df_tvshows_roto = df_tvshows.copy()

df_tvshows_roto.drop(df_tvshows_roto.loc[df_tvshows_roto['Rotten Tomatoes'] == "NA"].index, inplace = True)
# df_tvshows_roto = df_tvshows_roto[df_tvshows_roto.Rotten Tomatoes != "NA"]
df_tvshows_roto['Rotten Tomatoes'] = df_tvshows_roto['Rotten Tomatoes'].astype(int)

# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_roto_tvshows = df_tvshows_roto.loc[df_tvshows_roto['Netflix'] == 1]
hulu_roto_tvshows = df_tvshows_roto.loc[df_tvshows_roto['Hulu'] == 1]
prime_video_roto_tvshows = df_tvshows_roto.loc[df_tvshows_roto['Prime Video'] == 1]
disney_roto_tvshows = df_tvshows_roto.loc[df_tvshows_roto['Disney+'] == 1]

df_tvshows_roto_group = df_tvshows_roto.copy()

plt.figure(figsize = (10, 10))
corr = df_tvshows_roto.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>

df_roto_high_tvshows = df_tvshows_roto.sort_values(by = 'Rotten Tomatoes', ascending = False).reset_index()
df_roto_high_tvshows = df_roto_high_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_roto['Rotten Tomatoes'] == (df_tvshows_roto['Rotten Tomatoes'].max()))
# df_roto_high_tvshows = df_tvshows_roto[filter]
 
# highest_rated_tvshows = df_tvshows_roto.loc[df_tvshows_roto['Rotten Tomatoes'].idxmax()]
 
print('\nTV Shows with Highest Ever Rotten Tomatoes  are : \n')
df_roto_high_tvshows.head(5)

TV Shows with Highest Ever Rotten Tomatoes  are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	2275	Gravity Falls	2012	7	8.9	100	NA	Jason Ritter,Alex Hirsch,Kristen Schaal,Linda ...	Animation,Adventure,Comedy,Drama,Family,Fantas...	United States,Argentina,Australia,United Kingd...	...	In a world populated with superhumans, the sup...	23	tv series	2	0	1	0	1	1	Disney+
1	791	Hyperdrive	2019	7	6.7	100	NA	Nick Frost,Kevin Eldon,Miranda Hart,Dan Antopo...	Comedy,Sci-Fi	United Kingdom	...	As a gifted young football athlete from Bristo...	29	tv series	2	1	0	0	0	1	Netflix
2	2345	Cowboy Bebop	1998	7	8.9	100	NA	Kôichi Yamadera,Unshô Ishizuka,Steve Blum,Beau...	Animation,Action,Adventure,Comedy,Drama,Sci-Fi...	Japan	...	From the earliest times, the humanity knows ab...	24	tv series	1	0	1	0	0	1	Hulu
3	2340	Elfen Lied	2004	16	8	100	NA	Sanae Kobayashi,Chihiro Suzuki,Mamiko Noto,Ada...	Animation,Action,Drama,Horror,Mystery,Sci-Fi,T...	Japan	...	NA	24	tv series	1	0	1	1	0	1	Prime Video
4	2322	Spaced	1999	16	8.5	100	NA	Jessica Hynes,Simon Pegg,Julia Deakin,Nick Fro...	Action,Comedy	United Kingdom	...	The numerous miraculous rescues by the local w...	25	tv series	2	0	1	0	0	1	Hulu

5 rows × 21 columns

fig = px.bar(y = df_roto_high_tvshows['Title'][:15],
             x = df_roto_high_tvshows['Rotten Tomatoes'][:15], 
             color = df_roto_high_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Highest Rotten Tomatoes : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

df_roto_low_tvshows = df_tvshows_roto.sort_values(by = 'Rotten Tomatoes', ascending = True).reset_index()
df_roto_low_tvshows = df_roto_low_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_roto['Rotten Tomatoes'] == (df_tvshows_roto['Rotten Tomatoes'].min()))
# df_roto_low_tvshows = df_tvshows_roto[filter]

print('\nTV Shows with Lowest Ever Rotten Tomatoes  are : \n')
df_roto_low_tvshows.head(5)

TV Shows with Lowest Ever Rotten Tomatoes  are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	1804	Real Rob	2015	18	6.5	0	NA	Rob Schneider,Patricia Maya Schneider,Jamie Li...	Comedy	United States	...	NA	30	tv series	2	1	0	0	0	1	Netflix
1	5120	Baby Talk	2017	NR	4.2	0	NA	Tony Danza,Paul Jessup,Ryan Jessup,Mary Page K...	Comedy	United States	...	Around the Next Bend is the story of two frien...	30	tv series	2	0	0	1	0	1	Prime Video
2	119	Switching Goals	1999	0	5.1	0	Mike Jeavons	Mike Jeavons	Action,Comedy,Family,Romance	NA	...	NA	12	tv series	NA	0	1	0	0	1	Hulu
3	10	Rainbow	2015	NR	7	0	Nagesh Kukunoor	Geoffrey Hayes,Roy Skelton,Stanley Bates,Rod B...	Animation,Family	United Kingdom	...	NA	22	tv series	20	1	0	0	0	1	Netflix
4	4429	Baby Talk	1991	0	4.2	0	NA	Tony Danza,Paul Jessup,Ryan Jessup,Mary Page K...	Comedy	United States	...	NA	30	tv series	2	0	0	1	0	1	Prime Video

5 rows × 21 columns

fig = px.bar(y = df_roto_low_tvshows['Title'][:15],
             x = df_roto_low_tvshows['Rotten Tomatoes'][:15], 
             color = df_roto_low_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Lowest Rotten Tomatoes : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      Total '{df_tvshows_roto['Rotten Tomatoes'].unique().shape[0]}' unique Rotten Tomatoes s were Given, They were Like this,\n
      
{df_tvshows_roto.sort_values(by = 'Rotten Tomatoes', ascending = False)['Rotten Tomatoes'].unique()}\n
 
      The Highest Ever Rotten Tomatoes Ever Any TV Show Got is '{df_roto_high_tvshows['Title'][0]}' : '{df_roto_high_tvshows['Rotten Tomatoes'].max()}'\n
 
      The Lowest Ever Rotten Tomatoes Ever Any TV Show Got is '{df_roto_low_tvshows['Title'][0]}' : '{df_roto_low_tvshows['Rotten Tomatoes'].min()}'\n
      ''')

      Total '92' unique Rotten Tomatoes s were Given, They were Like this,

      
[100  99  98  97  96  95  94  93  92  91  90  89  88  87  86  85  84  83
  82  81  80  79  78  77  76  75  74  73  72  71  70  69  68  67  66  65
  64  63  62  61  60  59  58  57  56  55  54  53  52  51  50  49  48  47
  46  45  44  43  42  41  40  39  38  37  36  35  34  33  32  31  30  29
  28  27  26  25  24  23  22  21  20  19  18  17  14  12  11  10   9   8
   6   0]

 
      The Highest Ever Rotten Tomatoes Ever Any TV Show Got is 'Gravity Falls' : '100'

 
      The Lowest Ever Rotten Tomatoes Ever Any TV Show Got is 'Real Rob' : '0'

      

netflix_roto_high_tvshows = df_roto_high_tvshows.loc[df_roto_high_tvshows['Netflix']==1].reset_index()
netflix_roto_high_tvshows = netflix_roto_high_tvshows.drop(['index'], axis = 1)
 
netflix_roto_low_tvshows = df_roto_low_tvshows.loc[df_roto_low_tvshows['Netflix']==1].reset_index()
netflix_roto_low_tvshows = netflix_roto_low_tvshows.drop(['index'], axis = 1)
 
netflix_roto_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	791	Hyperdrive	2019	7	6.7	100	NA	Nick Frost,Kevin Eldon,Miranda Hart,Dan Antopo...	Comedy,Sci-Fi	United Kingdom	...	As a gifted young football athlete from Bristo...	29	tv series	2	1	0	0	0	1	Netflix
1	593	Fauda	2015	18	8.2	100	NA	Lior Raz,Itzik Cohen,Neta Garty,Rona-Lee Shim'...	Action,Drama,Thriller	Israel	...	In crime ridden Gotham City, Thomas and Martha...	60	tv series	3	1	0	0	0	1	Netflix
2	784	The Hollow	2018	7	7.2	100	NA	Ashleigh Ball,Connor Parnall,Adrian Petriw,Mar...	Animation,Drama,Family,Mystery,Sci-Fi	Canada	...	A rare atmospheric phenomenon allows a New Yor...	24	tv series	2	1	0	0	0	1	Netflix
3	792	Time: The Kalief Browder Story	2017	16	8.5	100	NA	Kalief Browder,Venida Browder,Jay-Z,Paul Prest...	Documentary,Biography	United States	...	NA	NA	tv series	1	1	0	0	0	1	Netflix
4	782	Flowers	2016	16	8.2	100	NA	Sophia Di Martino,Olivia Colman,Julian Barratt...	Comedy,Drama	United Kingdom	...	NA	30	tv series	2	1	0	0	0	1	Netflix

5 rows × 21 columns

fig = px.bar(y = netflix_roto_high_tvshows['Title'][:15],
             x = netflix_roto_high_tvshows['Rotten Tomatoes'][:15], 
             color = netflix_roto_high_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Highest Rotten Tomatoes : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = netflix_roto_low_tvshows['Title'][:15],
             x = netflix_roto_low_tvshows['Rotten Tomatoes'][:15], 
             color = netflix_roto_low_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Lowest Rotten Tomatoes : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

hulu_roto_high_tvshows = df_roto_high_tvshows.loc[df_roto_high_tvshows['Hulu']==1].reset_index()
hulu_roto_high_tvshows = hulu_roto_high_tvshows.drop(['index'], axis = 1)
 
hulu_roto_low_tvshows = df_roto_low_tvshows.loc[df_roto_low_tvshows['Hulu']==1].reset_index()
hulu_roto_low_tvshows = hulu_roto_low_tvshows.drop(['index'], axis = 1)
 
hulu_roto_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	2275	Gravity Falls	2012	7	8.9	100	NA	Jason Ritter,Alex Hirsch,Kristen Schaal,Linda ...	Animation,Adventure,Comedy,Drama,Family,Fantas...	United States,Argentina,Australia,United Kingd...	...	In a world populated with superhumans, the sup...	23	tv series	2	0	1	0	1	1	Disney+
1	2345	Cowboy Bebop	1998	7	8.9	100	NA	Kôichi Yamadera,Unshô Ishizuka,Steve Blum,Beau...	Animation,Action,Adventure,Comedy,Drama,Sci-Fi...	Japan	...	From the earliest times, the humanity knows ab...	24	tv series	1	0	1	0	0	1	Hulu
2	2340	Elfen Lied	2004	16	8	100	NA	Sanae Kobayashi,Chihiro Suzuki,Mamiko Noto,Ada...	Animation,Action,Drama,Horror,Mystery,Sci-Fi,T...	Japan	...	NA	24	tv series	1	0	1	1	0	1	Prime Video
3	2322	Spaced	1999	16	8.5	100	NA	Jessica Hynes,Simon Pegg,Julia Deakin,Nick Fro...	Action,Comedy	United Kingdom	...	The numerous miraculous rescues by the local w...	25	tv series	2	0	1	0	0	1	Hulu
4	2318	Tokyo Ghoul	2014	18	7.8	100	NA	Natsuki Hanae,Austin Tindle,Sora Amamiya,Brina...	Animation,Action,Drama,Fantasy,Horror,Thriller	Japan	...	The series follows John Nolan, a 40-year-old m...	24	tv series	1	0	1	0	0	1	Hulu

5 rows × 21 columns

fig = px.bar(y = hulu_roto_high_tvshows['Title'][:15],
             x = hulu_roto_high_tvshows['Rotten Tomatoes'][:15], 
             color = hulu_roto_high_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Highest Rotten Tomatoes : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = hulu_roto_low_tvshows['Title'][:15],
             x = hulu_roto_low_tvshows['Rotten Tomatoes'][:15], 
             color = hulu_roto_low_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Lowest Rotten Tomatoes : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

prime_video_roto_high_tvshows = df_roto_high_tvshows.loc[df_roto_high_tvshows['Prime Video']==1].reset_index()
prime_video_roto_high_tvshows = prime_video_roto_high_tvshows.drop(['index'], axis = 1)
 
prime_video_roto_low_tvshows = df_roto_low_tvshows.loc[df_roto_low_tvshows['Prime Video']==1].reset_index()
prime_video_roto_low_tvshows = prime_video_roto_low_tvshows.drop(['index'], axis = 1)
 
prime_video_roto_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	2340	Elfen Lied	2004	16	8	100	NA	Sanae Kobayashi,Chihiro Suzuki,Mamiko Noto,Ada...	Animation,Action,Drama,Horror,Mystery,Sci-Fi,T...	Japan	...	NA	24	tv series	1	0	1	1	0	1	Prime Video
1	321	Utopia	1951	7	6.7	100	NA	John Cusack,Ashleigh LaThrop,Dan Byrd,Desmin B...	Action,Drama,Mystery,Sci-Fi,Thriller	United States	...	An acclaimed documentary feature exploring the...	55	tv series	1	0	0	1	0	1	Prime Video
2	2288	Mr. Bean	1990	7	8.5	100	NA	Rowan Atkinson,Robin Driscoll,Matilda Ziegler,...	Comedy,Family	United Kingdom	...	The story of an inner-city Los Angeles police ...	25	tv series	1	0	1	1	0	1	Prime Video
3	2511	Yu Yu Hakusho	1992	7	8.5	100	NA	Nozomu Sasaki,Justin Cook,Christopher Sabat,Cy...	Animation,Action,Adventure,Comedy,Drama,Fantas...	NA	...	Follows a deep-cover operative named Martin Od...	24	tv series	1	0	1	1	0	1	Prime Video
4	199	Sanctuary	2017	0	7.3	100	NA	Amanda Tapping,Robin Dunne,Christopher Heyerda...	Action,Drama,Fantasy,Mystery,Sci-Fi	Canada	...	NA	44	tv series	4	0	0	1	0	1	Prime Video

5 rows × 21 columns

fig = px.bar(y = prime_video_roto_high_tvshows['Title'][:15],
             x = prime_video_roto_high_tvshows['Rotten Tomatoes'][:15], 
             color = prime_video_roto_high_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Highest Rotten Tomatoes : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = prime_video_roto_low_tvshows['Title'][:15],
             x = prime_video_roto_low_tvshows['Rotten Tomatoes'][:15], 
             color = prime_video_roto_low_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Lowest Rotten Tomatoes : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

disney_roto_high_tvshows = df_roto_high_tvshows.loc[df_roto_high_tvshows['Disney+']==1].reset_index()
disney_roto_high_tvshows = disney_roto_high_tvshows.drop(['index'], axis = 1)
 
disney_roto_low_tvshows = df_roto_low_tvshows.loc[df_roto_low_tvshows['Disney+']==1].reset_index()
disney_roto_low_tvshows = disney_roto_low_tvshows.drop(['index'], axis = 1)
 
disney_roto_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	2275	Gravity Falls	2012	7	8.9	100	NA	Jason Ritter,Alex Hirsch,Kristen Schaal,Linda ...	Animation,Adventure,Comedy,Drama,Family,Fantas...	United States,Argentina,Australia,United Kingd...	...	In a world populated with superhumans, the sup...	23	tv series	2	0	1	0	1	1	Disney+
1	5308	Lizzie McGuire	2001	7	6.6	100	NA	Hilary Duff,Lalaine,Adam Lamberg,Jake Thomas,H...	Comedy,Drama,Family	United States	...	NA	30	tv series	2	0	0	0	1	1	Disney+
2	5318	Disney Gallery / Star Wars: The Mandalorian	2020	7	8.5	100	Josiah Swanson	Josiah Swanson	Talk-Show	NA	...	NA	NA	tv series	NA	0	0	0	1	1	Disney+
3	5301	DuckTales	2017	0	8.3	100	NA	David Tennant,Ben Schwartz,Danny Pudi,Bobby Mo...	Animation,Action,Adventure,Comedy,Family,Fanta...	United States	...	NA	21	tv series	3	0	0	0	1	1	Disney+
4	5304	The Imagineering Story	2019	0	9	100	NA	Tom Morris,Kevin Rafferty,Angela Bassett,Tom F...	Documentary	United States	...	Raven Baxter is a teenager. She can see glimps...	60	tv series	1	0	0	0	1	1	Disney+

5 rows × 21 columns

fig = px.bar(y = disney_roto_high_tvshows['Title'][:15],
             x = disney_roto_high_tvshows['Rotten Tomatoes'][:15], 
             color = disney_roto_high_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Highest Rotten Tomatoes : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = disney_roto_low_tvshows['Title'][:15],
             x = disney_roto_low_tvshows['Rotten Tomatoes'][:15], 
             color = disney_roto_low_tvshows['Rotten Tomatoes'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Lowest Rotten Tomatoes : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      The TV Show with Highest Rotten Tomatoes  Ever Got is '{df_roto_high_tvshows['Title'][0]}' : '{df_roto_high_tvshows['Rotten Tomatoes'].max()}'\n
      The TV Show with Lowest Rotten Tomatoes  Ever Got is '{df_roto_low_tvshows['Title'][0]}' : '{df_roto_low_tvshows['Rotten Tomatoes'].min()}'\n
      
      The TV Show with Highest Rotten Tomatoes  on 'Netflix' is '{netflix_roto_high_tvshows['Title'][0]}' : '{netflix_roto_high_tvshows['Rotten Tomatoes'].max()}'\n
      The TV Show with Lowest Rotten Tomatoes  on 'Netflix' is '{netflix_roto_low_tvshows['Title'][0]}' : '{netflix_roto_low_tvshows['Rotten Tomatoes'].min()}'\n
      
      The TV Show with Highest Rotten Tomatoes  on 'Hulu' is '{hulu_roto_high_tvshows['Title'][0]}' : '{hulu_roto_high_tvshows['Rotten Tomatoes'].max()}'\n
      The TV Show with Lowest Rotten Tomatoes  on 'Hulu' is '{hulu_roto_low_tvshows['Title'][0]}' : '{hulu_roto_low_tvshows['Rotten Tomatoes'].min()}'\n
      
      The TV Show with Highest Rotten Tomatoes  on 'Prime Video' is '{prime_video_roto_high_tvshows['Title'][0]}' : '{prime_video_roto_high_tvshows['Rotten Tomatoes'].max()}'\n
      The TV Show with Lowest Rotten Tomatoes  on 'Prime Video' is '{prime_video_roto_low_tvshows['Title'][0]}' : '{prime_video_roto_low_tvshows['Rotten Tomatoes'].min()}'\n
      
      The TV Show with Highest Rotten Tomatoes  on 'Disney+' is '{disney_roto_high_tvshows['Title'][0]}' : '{disney_roto_high_tvshows['Rotten Tomatoes'].max()}'\n
      The TV Show with Lowest Rotten Tomatoes  on 'Disney+' is '{disney_roto_low_tvshows['Title'][0]}' : '{disney_roto_low_tvshows['Rotten Tomatoes'].min()}'\n 
      ''')

      The TV Show with Highest Rotten Tomatoes  Ever Got is 'Gravity Falls' : '100'

      The TV Show with Lowest Rotten Tomatoes  Ever Got is 'Real Rob' : '0'

      
      The TV Show with Highest Rotten Tomatoes  on 'Netflix' is 'Hyperdrive' : '100'

      The TV Show with Lowest Rotten Tomatoes  on 'Netflix' is 'Real Rob' : '0'

      
      The TV Show with Highest Rotten Tomatoes  on 'Hulu' is 'Gravity Falls' : '100'

      The TV Show with Lowest Rotten Tomatoes  on 'Hulu' is 'Switching Goals' : '0'

      
      The TV Show with Highest Rotten Tomatoes  on 'Prime Video' is 'Elfen Lied' : '100'

      The TV Show with Lowest Rotten Tomatoes  on 'Prime Video' is 'Baby Talk' : '0'

      
      The TV Show with Highest Rotten Tomatoes  on 'Disney+' is 'Gravity Falls' : '100'

      The TV Show with Lowest Rotten Tomatoes  on 'Disney+' is 'Zapped' : '6'
 
      

print(f'''
      Accross All Platforms the Average Rotten Tomatoes  is '{round(df_tvshows_roto['Rotten Tomatoes'].mean(), ndigits = 2)}'\n
      The Average Rotten Tomatoes  on 'Netflix' is '{round(netflix_roto_tvshows['Rotten Tomatoes'].mean(), ndigits = 2)}'\n
      The Average Rotten Tomatoes  on 'Hulu' is '{round(hulu_roto_tvshows['Rotten Tomatoes'].mean(), ndigits = 2)}'\n
      The Average Rotten Tomatoes  on 'Prime Video' is '{round(prime_video_roto_tvshows['Rotten Tomatoes'].mean(), ndigits = 2)}'\n
      The Average Rotten Tomatoes  on 'Disney+' is '{round(disney_roto_tvshows['Rotten Tomatoes'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Rotten Tomatoes  is '75.52'

      The Average Rotten Tomatoes  on 'Netflix' is '77.96'

      The Average Rotten Tomatoes  on 'Hulu' is '77.04'

      The Average Rotten Tomatoes  on 'Prime Video' is '71.16'

      The Average Rotten Tomatoes  on 'Disney+' is '76.23'
 
      

f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_roto['Rotten Tomatoes'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_roto['Rotten Tomatoes'], ax = ax[1])
plt.show()

# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Rotten Tomatoes s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_roto_tvshows['Rotten Tomatoes'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_roto_tvshows['Rotten Tomatoes'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_roto_tvshows['Rotten Tomatoes'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_roto_tvshows['Rotten Tomatoes'][:100], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()

def round_val(data):
    if str(data) != 'nan':
        return round(data)
        
def round_fix(data):
    if data in range(0,11):
        # print(data)
        return 10
    if data in range(11,21):
        return 20
    if data in range(21,31):
        return 30
    if data in range(31,41):
        return 40
    if data in range(41,51):
        return 50
    if data in range(51,61):
        return 60
    if data in range(61,71):
        return 70
    if data in range(71,81):
        return 80
    if data in range(81,91):
        return 90
    if data in range(91,101):
        return 100

df_tvshows_roto_group['Rotten Tomatoes Group'] = df_tvshows_roto['Rotten Tomatoes'].apply(round_fix)
 
roto_values = df_tvshows_roto_group['Rotten Tomatoes Group'].value_counts().sort_index(ascending = False).tolist()
roto_index = df_tvshows_roto_group['Rotten Tomatoes Group'].value_counts().sort_index(ascending = False).index
 
# roto_values, roto_index

roto_group_count = df_tvshows_roto_group.groupby('Rotten Tomatoes Group')['Title'].count()
roto_group_tvshows = df_tvshows_roto_group.groupby('Rotten Tomatoes Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
roto_group_data_tvshows = pd.concat([roto_group_count, roto_group_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
roto_group_data_tvshows = roto_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

# Rotten Tomatoes Group with TV Shows Counts - All Platforms Combined
roto_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

	Rotten Tomatoes Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
9	100	393	194	123	96	11
8	90	291	115	104	89	9
7	80	164	65	62	49	1
6	70	107	35	35	35	4
5	60	78	29	26	28	1
4	50	68	26	16	29	2
3	40	60	26	15	20	0
1	20	30	9	5	16	2
2	30	29	10	6	14	0
0	10	18	7	7	6	1

roto_group_data_tvshows.sort_values(by = 'Rotten Tomatoes Group', ascending = False)

	Rotten Tomatoes Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
9	100	393	194	123	96	11
8	90	291	115	104	89	9
7	80	164	65	62	49	1
6	70	107	35	35	35	4
5	60	78	29	26	28	1
4	50	68	26	16	29	2
3	40	60	26	15	20	0
2	30	29	10	6	14	0
1	20	30	9	5	16	2
0	10	18	7	7	6	1

fig = px.bar(y = roto_group_data_tvshows['TV Shows Count'],
             x = roto_group_data_tvshows['Rotten Tomatoes Group'], 
             color = roto_group_data_tvshows['Rotten Tomatoes Group'],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows Count', 'x' : 'Rotten Tomatoes : Rating'},
             title  = 'TV Shows with Group Rotten Tomatoes : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()

fig = px.pie(roto_group_data_tvshows[:10],
             names = roto_group_data_tvshows['Rotten Tomatoes Group'],
             values = roto_group_data_tvshows['TV Shows Count'],
             color = roto_group_data_tvshows['TV Shows Count'],
             color_discrete_sequence = px.colors.sequential.Teal)

fig.update_traces(textinfo = 'percent+label',
                  title = 'TV Shows Count based on Rotten Tomatoes Group')
fig.show()

df_roto_group_high_tvshows = roto_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_roto_group_high_tvshows = df_roto_group_high_tvshows.drop(['index'], axis = 1)
# filter = (roto_group_data_tvshows['TV Shows Count'] ==  (roto_group_data_tvshows['TV Shows Count'].max()))
# df_roto_group_high_tvshows = roto_group_data_tvshows[filter]
 
# highest_rated_tvshows = roto_group_data_tvshows.loc[roto_group_data_tvshows['TV Shows Count'].idxmax()]
 
# print('\nRotten Tomatoes with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_roto_group_high_tvshows.head(5)

	Rotten Tomatoes Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	100	393	194	123	96	11
1	90	291	115	104	89	9
2	80	164	65	62	49	1
3	70	107	35	35	35	4
4	60	78	29	26	28	1

df_roto_group_low_tvshows = roto_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_roto_group_low_tvshows = df_roto_group_low_tvshows.drop(['index'], axis = 1)
# filter = (roto_group_data_tvshows['TV Shows Count'] = =  (roto_group_data_tvshows['TV Shows Count'].min()))
# df_roto_group_low_tvshows = roto_group_data_tvshows[filter]
 
# print('\nRotten Tomatoes with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_roto_group_low_tvshows.head(5)

	Rotten Tomatoes Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	10	18	7	7	6	1
1	30	29	10	6	14	0
2	20	30	9	5	16	2
3	40	60	26	15	20	0
4	50	68	26	16	29	2

print(f'''
      Total '{df_tvshows_roto['Rotten Tomatoes'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see TV Shows from Total '{roto_group_data_tvshows['Rotten Tomatoes Group'].unique().shape[0]}' Rotten Tomatoes Group, They were Like this, \n
 
      {roto_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Rotten Tomatoes Group'].unique()} etc. \n
 
      The Rotten Tomatoes Group with Highest TV Shows Count have '{roto_group_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_roto_group_high_tvshows['Rotten Tomatoes Group'][0]}', &\n
      The Rotten Tomatoes Group with Lowest TV Shows Count have '{roto_group_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_roto_group_low_tvshows['Rotten Tomatoes Group'][0]}'
      ''')

      Total '1238' Titles are available on All Platforms, out of which

      You Can Choose to see TV Shows from Total '10' Rotten Tomatoes Group, They were Like this, 

 
      [100  90  80  70  60  50  40  20  30  10] etc. 

 
      The Rotten Tomatoes Group with Highest TV Shows Count have '393' TV Shows Available is '100', &

      The Rotten Tomatoes Group with Lowest TV Shows Count have '18' TV Shows Available is '10'
      

netflix_roto_group_tvshows = roto_group_data_tvshows[roto_group_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_roto_group_tvshows = netflix_roto_group_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_roto_group_high_tvshows = df_roto_group_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_roto_group_high_tvshows = netflix_roto_group_high_tvshows.drop(['index'], axis = 1)
 
netflix_roto_group_low_tvshows = df_roto_group_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_roto_group_low_tvshows = netflix_roto_group_low_tvshows.drop(['index'], axis = 1)
 
netflix_roto_group_high_tvshows.head(5)

	Rotten Tomatoes Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	100	393	194	123	96	11
1	90	291	115	104	89	9
2	80	164	65	62	49	1
3	70	107	35	35	35	4
4	60	78	29	26	28	1

hulu_roto_group_tvshows = roto_group_data_tvshows[roto_group_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_roto_group_tvshows = hulu_roto_group_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_roto_group_high_tvshows = df_roto_group_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_roto_group_high_tvshows = hulu_roto_group_high_tvshows.drop(['index'], axis = 1)
 
hulu_roto_group_low_tvshows = df_roto_group_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_roto_group_low_tvshows = hulu_roto_group_low_tvshows.drop(['index'], axis = 1)
 
hulu_roto_group_high_tvshows.head(5)

	Rotten Tomatoes Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	100	393	194	123	96	11
1	90	291	115	104	89	9
2	80	164	65	62	49	1
3	70	107	35	35	35	4
4	60	78	29	26	28	1

prime_video_roto_group_tvshows = roto_group_data_tvshows[roto_group_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_roto_group_tvshows = prime_video_roto_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_roto_group_high_tvshows = df_roto_group_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_roto_group_high_tvshows = prime_video_roto_group_high_tvshows.drop(['index'], axis = 1)
 
prime_video_roto_group_low_tvshows = df_roto_group_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_roto_group_low_tvshows = prime_video_roto_group_low_tvshows.drop(['index'], axis = 1)
 
prime_video_roto_group_high_tvshows.head(5)

	Rotten Tomatoes Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	100	393	194	123	96	11
1	90	291	115	104	89	9
2	80	164	65	62	49	1
3	70	107	35	35	35	4
4	50	68	26	16	29	2

disney_roto_group_tvshows = roto_group_data_tvshows[roto_group_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_roto_group_tvshows = disney_roto_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_roto_group_high_tvshows = df_roto_group_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_roto_group_high_tvshows = disney_roto_group_high_tvshows.drop(['index'], axis = 1)
 
disney_roto_group_low_tvshows = df_roto_group_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_roto_group_low_tvshows = disney_roto_group_low_tvshows.drop(['index'], axis = 1)
 
disney_roto_group_high_tvshows.head(5)

	Rotten Tomatoes Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	100	393	194	123	96	11
1	90	291	115	104	89	9
2	70	107	35	35	35	4
3	50	68	26	16	29	2
4	20	30	9	5	16	2

print(f'''
      The Rotten Tomatoes Group with Highest TV Shows Count Ever Got is '{df_roto_group_high_tvshows['Rotten Tomatoes Group'][0]}' : '{df_roto_group_high_tvshows['TV Shows Count'].max()}'\n
      The Rotten Tomatoes Group with Lowest TV Shows Count Ever Got is '{df_roto_group_low_tvshows['Rotten Tomatoes Group'][0]}' : '{df_roto_group_low_tvshows['TV Shows Count'].min()}'\n
      
      The Rotten Tomatoes Group with Highest TV Shows Count on 'Netflix' is '{netflix_roto_group_high_tvshows['Rotten Tomatoes Group'][0]}' : '{netflix_roto_group_high_tvshows['Netflix'].max()}'\n
      The Rotten Tomatoes Group with Lowest TV Shows Count on 'Netflix' is '{netflix_roto_group_low_tvshows['Rotten Tomatoes Group'][0]}' : '{netflix_roto_group_low_tvshows['Netflix'].min()}'\n
      
      The Rotten Tomatoes Group with Highest TV Shows Count on 'Hulu' is '{hulu_roto_group_high_tvshows['Rotten Tomatoes Group'][0]}' : '{hulu_roto_group_high_tvshows['Hulu'].max()}'\n
      The Rotten Tomatoes Group with Lowest TV Shows Count on 'Hulu' is '{hulu_roto_group_low_tvshows['Rotten Tomatoes Group'][0]}' : '{hulu_roto_group_low_tvshows['Hulu'].min()}'\n
      
      The Rotten Tomatoes Group with Highest TV Shows Count on 'Prime Video' is '{prime_video_roto_group_high_tvshows['Rotten Tomatoes Group'][0]}' : '{prime_video_roto_group_high_tvshows['Prime Video'].max()}'\n
      The Rotten Tomatoes Group with Lowest TV Shows Count on 'Prime Video' is '{prime_video_roto_group_low_tvshows['Rotten Tomatoes Group'][0]}' : '{prime_video_roto_group_low_tvshows['Prime Video'].min()}'\n
      
      The Rotten Tomatoes Group with Highest TV Shows Count on 'Disney+' is '{disney_roto_group_high_tvshows['Rotten Tomatoes Group'][0]}' : '{disney_roto_group_high_tvshows['Disney+'].max()}'\n
      The Rotten Tomatoes Group with Lowest TV Shows Count on 'Disney+' is '{disney_roto_group_low_tvshows['Rotten Tomatoes Group'][0]}' : '{disney_roto_group_low_tvshows['Disney+'].min()}'\n 
      ''')

      The Rotten Tomatoes Group with Highest TV Shows Count Ever Got is '100' : '393'

      The Rotten Tomatoes Group with Lowest TV Shows Count Ever Got is '10' : '18'

      
      The Rotten Tomatoes Group with Highest TV Shows Count on 'Netflix' is '100' : '194'

      The Rotten Tomatoes Group with Lowest TV Shows Count on 'Netflix' is '10' : '7'

      
      The Rotten Tomatoes Group with Highest TV Shows Count on 'Hulu' is '100' : '123'

      The Rotten Tomatoes Group with Lowest TV Shows Count on 'Hulu' is '20' : '5'

      
      The Rotten Tomatoes Group with Highest TV Shows Count on 'Prime Video' is '100' : '96'

      The Rotten Tomatoes Group with Lowest TV Shows Count on 'Prime Video' is '10' : '6'

      
      The Rotten Tomatoes Group with Highest TV Shows Count on 'Disney+' is '100' : '11'

      The Rotten Tomatoes Group with Lowest TV Shows Count on 'Disney+' is '40' : '0'
 
      

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ro_ax1 = sns.barplot(x = netflix_roto_group_tvshows['Rotten Tomatoes Group'][:10], y = netflix_roto_group_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_ro_ax2 = sns.barplot(x = hulu_roto_group_tvshows['Rotten Tomatoes Group'][:10], y = hulu_roto_group_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_ro_ax3 = sns.barplot(x = prime_video_roto_group_tvshows['Rotten Tomatoes Group'][:10], y = prime_video_roto_group_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_ro_ax4 = sns.barplot(x = disney_roto_group_tvshows['Rotten Tomatoes Group'][:10], y = disney_roto_group_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ro_ax1.title.set_text(labels[0])
h_ro_ax2.title.set_text(labels[1])
p_ro_ax3.title.set_text(labels[2])
d_ro_ax4.title.set_text(labels[3])
 
plt.show()

plt.figure(figsize = (20, 5))
sns.lineplot(x = roto_group_data_tvshows['Rotten Tomatoes Group'], y = roto_group_data_tvshows['Netflix'], color = 'red')
sns.lineplot(x = roto_group_data_tvshows['Rotten Tomatoes Group'], y = roto_group_data_tvshows['Hulu'], color = 'lightgreen')
sns.lineplot(x = roto_group_data_tvshows['Rotten Tomatoes Group'], y = roto_group_data_tvshows['Prime Video'], color = 'lightblue')
sns.lineplot(x = roto_group_data_tvshows['Rotten Tomatoes Group'], y = roto_group_data_tvshows['Disney+'], color = 'darkblue')
plt.xlabel('Rotten Tomatoes Group', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()

print(f'''
      Accross All Platforms Total Count of Rotten Tomatoes Group is '{roto_group_data_tvshows['Rotten Tomatoes Group'].unique().shape[0]}'\n
      Total Count of Rotten Tomatoes Group on 'Netflix' is '{netflix_roto_group_tvshows['Rotten Tomatoes Group'].unique().shape[0]}'\n
      Total Count of Rotten Tomatoes Group on 'Hulu' is '{hulu_roto_group_tvshows['Rotten Tomatoes Group'].unique().shape[0]}'\n
      Total Count of Rotten Tomatoes Group on 'Prime Video' is '{prime_video_roto_group_tvshows['Rotten Tomatoes Group'].unique().shape[0]}'\n
      Total Count of Rotten Tomatoes Group on 'Disney+' is '{disney_roto_group_tvshows['Rotten Tomatoes Group'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Rotten Tomatoes Group is '10'

      Total Count of Rotten Tomatoes Group on 'Netflix' is '10'

      Total Count of Rotten Tomatoes Group on 'Hulu' is '10'

      Total Count of Rotten Tomatoes Group on 'Prime Video' is '10'

      Total Count of Rotten Tomatoes Group on 'Disney+' is '8'
 
      

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ro_ax1 = sns.lineplot(y = roto_group_data_tvshows['Rotten Tomatoes Group'], x = roto_group_data_tvshows['Netflix'], color = 'red', ax = axes[0, 0])
h_ro_ax2 = sns.lineplot(y = roto_group_data_tvshows['Rotten Tomatoes Group'], x = roto_group_data_tvshows['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_ro_ax3 = sns.lineplot(y = roto_group_data_tvshows['Rotten Tomatoes Group'], x = roto_group_data_tvshows['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_ro_ax4 = sns.lineplot(y = roto_group_data_tvshows['Rotten Tomatoes Group'], x = roto_group_data_tvshows['Disney+'], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ro_ax1.title.set_text(labels[0])
h_ro_ax2.title.set_text(labels[1])
p_ro_ax3.title.set_text(labels[2])
d_ro_ax4.title.set_text(labels[3])

plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ro_ax1 = sns.barplot(x = roto_group_data_tvshows['Rotten Tomatoes Group'][:10], y = roto_group_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_ro_ax2 = sns.barplot(x = roto_group_data_tvshows['Rotten Tomatoes Group'][:10], y = roto_group_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_ro_ax3 = sns.barplot(x = roto_group_data_tvshows['Rotten Tomatoes Group'][:10], y = roto_group_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_ro_ax4 = sns.barplot(x = roto_group_data_tvshows['Rotten Tomatoes Group'][:10], y = roto_group_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ro_ax1.title.set_text(labels[0])
h_ro_ax2.title.set_text(labels[1])
p_ro_ax3.title.set_text(labels[2])
d_ro_ax4.title.set_text(labels[3])
 
plt.show()